Title: Prediction of fatigue life of packaging EMC material based on RBF-SVM

Authors: Hai Guo; Jinghua Yin; Jingying Zhao; Zhiyu Huang; Yue Pan

Addresses: Harbin University of Science and Technology, 52 Xuefu Load, Nangang, Harbin, China; College of Computer Science and Engineering, Dalian Nationalities University, 18 Liaohe West Road, Dalian Development Zone, Dalian, 116600, China ' Harbin University of Science and Technology, 52 Xuefu Load, Nangang, Harbin, China ' College of Computer Science and Engineering, Dalian Nationalities University, 18 Liaohe West Road, Dalian Development Zone, Dalian, 116600, China ' College of Computer Science and Engineering, Dalian Nationalities University, 18 Liaohe West Road, Dalian Development Zone, Dalian, 116600, China ' College of Computer Science and Engineering, Dalian Nationalities University, 18 Liaohe West Road, Dalian Development Zone, Dalian, 116600, China

Abstract: This paper applies a radial basis function support vector machine (RBF-SVM) for predicting fatigue life of packaging elastic memory composites (EMC) material. EMC is one of the three dominating materials in the FOL packing. Using EMC to encapsulate the large-scale integrated circuit has predominated research in the areas all over the world. Till now, more than 95% of microelectronic devices are PEMs. In this study, a RBF-SVM model was constructed to predict life of EMC material and the penalty parameter C and nuclear parameter R was optimised. Meanwhile, a comparison between the predictions of BP neural network, polynomial kernel, sigmoid kernel, and RBF kernel was made. Experiments show that the SVM (RBF-SVM) has higher complexity and prediction accuracy than polynomial kernel and sigmoid kernel have, and its prediction accuracy is far higher than that of BP neural network model. According to the predictions, RBF-SVM is very suitable for applying in the prediction of fatigue life of packaging EMC material and receives good prediction complexity and accuracy.

Keywords: fatigue life; epoxy moulding compound; elastic memory composites; EMC; radial basis function SVM; support vector machines; RBF-SVM; prediction modelling; packaging materials; integrated circuits; neural networks; polynomial kernel; sigmoid kernel; RBF kernel.

DOI: 10.1504/IJMPT.2014.062934

International Journal of Materials and Product Technology, 2014 Vol.49 No.1, pp.5 - 17

Received: 17 Jul 2013
Accepted: 23 Oct 2013
Published online: 19 Jul 2014 *

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